Vibrational Alarms Facilitated by Determination of Motor On-Off State in Variable-Duty Multi-Motor Machines

ABSTRACT

Apparatus and associated methods relate to a vibrational sensing system (VSS) including an accelerometer and a data processor, which determines an “operational state” of a mechanical drive unit, the processor further employing the “operational state” to gate learning of long-term vibrational data to exclude collection of non-operational data, the long-term data collected to calculate alarm thresholds. For example, vibrations from a target motor are sensed by a coupled accelerometer. Vibrational data from the accelerometer is fed into a data processor which determines the operational state of the motor. The operational state (e.g., on/off indication) may gate data collection such that data is only acquired during on-time, which may advantageously create accurate baselines from which alarm thresholds may be generated, and nuisance alarms may be avoided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of, and claims the benefit of, U.S.application Ser. No. 16/109,441, titled “Vibrational Alarms Facilitatedby Determination of Motor On-Off State in Variable-Duty Multi-MotorMachines,” filed by Robert T. Fayfield, et al., on Aug. 22, 2018, whichclaims the benefit of U.S. Provisional Application Ser. No. 62/549,581titled “Vibrational Alarms Facilitated by Determination of Motor On-OffState in Variable-Duty Multi-Motor Machines,” filed by Fayfield, et al.on Aug. 24, 2017.

This application incorporates the entire contents of the foregoingapplication(s) herein by reference.

TECHNICAL FIELD

Various embodiments relate generally to vibration monitoring andvibration analytics.

BACKGROUND

Various mechanical drive units are employed in industry for a variety ofreasons. For example, conventional rotary motors may be employed to moveproducts through manufacturing, to move air, to perform variousmachining such as cutting, drilling, grinding, and to drive variousprocesses such as stirring, lifting and pumping. In some examples,linear motors may be employed to move various cams and shafts, or toperform various machining such as sanding and shaving. Mechanical driveunits such as motors and various vibrating machines may perform avariety of other useful tasks. In some examples, electronic controlunits may control the operational state of machines containingmechanical drive units.

Motors and other machines may produce vibrational motion as a sideeffect of their operation. Vibrations may be caused by slightimperfections in the components making up the machine or motors. Forexample, various bearings within rotating motors may containimperfections or may wear over time. These imperfections may also leadto various vibrational behavior. In some examples, the vibrations may beat the rotational frequency of the motor(s).

Some machines may contain multiple motors running simultaneously orrunning intermittent duty (cycling on/off). The on/off cycling of motorswithin a machine may be uncorrelated. In some industries, motorsemployed on the factory floor(s) may run uninterrupted for hours, andsome may be idle for hours. Some motors may be located in remote,difficult-to-access areas.

SUMMARY

Apparatus and associated methods relate to a vibrational sensing system(VSS) including an accelerometer and a data processor, which determinesan “operational state” of a mechanical drive unit, the processor furtheremploying the “operational state” to gate learning of long-termvibrational data to exclude collection of non-operational data, thelong-term data collected to calculate alarm thresholds. For example,vibrations from a target motor are sensed by a coupled accelerometer.Vibrational data from the accelerometer is fed into a data processorwhich determines the operational state of the motor. The operationalstate (e.g., on/off indication) may gate data collection such that datais only acquired during on-time, which may advantageously createaccurate baselines from which alarm thresholds may be generated, andnuisance alarms may be avoided.

Various embodiments may achieve one or more advantages. For example,some embodiments may autonomously determine the on-off state of a motorwithin a machine that contains one or more sources of vibration, therebyreliably implementing disturbance rejection of cross-coupled machines.Using this method, some embodiments may produce long-term vibrationalanalytics for motors under intermittent operation, by logging dataduring on-state (run-time) only, avoiding false alarms. In anillustrative example, the VSS may autonomously indicate a motor'srunning state based on vibration in a machine that contains multiplesources of vibration. The VSS may learn long-term trends of motorvibrations, thereby determining appropriate alarm thresholds, which maybe employed to provide a kind of “check engine light” for each motor.The alarms may avoid unexpected downtime of motors by recognizing longterm wear conditions and/or failure trends (e.g., imbalance,misalignment, looseness, bearing degradation) early on, giving time toschedule preventative maintenance and extended machine life.

The details of various embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a plan view of an exemplary vibrational sensing system(VSS) processing accelerometer data into a motor on-off signal.

FIGS. 2A and 2B depict schematic views of an exemplary accelerometerdata processor with an event filter enable.

FIGS. 3A and 3B depict schematic views of exemplary motor on-offdetermination circuits.

FIG. 4 depicts a block diagram of a binning scheme and failure diagnosisbased on frequency of an exemplary VSS data processing algorithm.

FIGS. 5A, 5B, 5C, and 5D depict schematic views of an exemplaryaccelerometer data processor with an event filter enable at fixed switchpositions.

FIGS. 6A, 6B, 6C, and 6D depict schematic views of an exemplaryaccelerometer data processor with an event filter enable at fixed switchpositions.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

To aid understanding, this document is organized as follows. First, anexemplary use case is briefly introduced with reference to FIG. 1. Next,with reference to FIGS. 2A and 2B, the discussion turns to examinationof data processing embodiments that illustrate various methods offiltering an accelerometer dataset to generate various alarms. In FIGS.3A and 3B the discussion turns to examination of exemplary functionalblocks that determine motor on-off based on the accelerometer dataset.FIG. 4 presents an exemplary embodiment that sorts the accelerometerdataset into bins based on frequency content. Specifically, withreference to FIG. 4, the binning splits the dataset into statisticallikelihoods of various machine failures. Finally, FIGS. 5A-5D and 6A-6Dpresent specific fixed switch positions of SW1 and SW2, for furtherclarification of FIGS. 2A and 2B.

FIG. 1 depicts a plan view of an exemplary vibrational sensing system(VSS) processing accelerometer data into a “motor on-off” signal. A VSS100 includes a target motor 105. The target motor 105 may be located atan end-customer's manufacturing facility, for example. The target motor105 may exist within a manufacturing machine, on a roof providingfacility ventilation, or in a variety of other applications. In someapplications, the customer may rely on the target motor 105 to continueprofitable manufacturing, for example. A VSS sensor 110 is fixedlycoupled to the target motor 105. In some examples, various methods maybe employed to couple the VSS sensor 110 to the target motor 105. TheVSS sensor 110 is in operable communication with a transmitter node 115.The VSS sensor 110 is operable to send overall trending parametric data,for example, RMS velocity data 120 and RMS high-pass acceleration data125, to the transmitter node 115. The transmitter node 115 sends theoverall trending parametric data 120 and 125 via a wireless link to acontroller receiver 130. The controller receiver 130 includes a signalprocessor operable to convert the data 120 and 125 to motor on-offindications (above the graphed data 125). The controller receiver 130includes an alarm output 145. In some embodiments, the controller may beoperable to produce various alarm outputs 145. Further detail on alarmoutputs can be found with reference to FIGS. 2A and 2B.

The controller receiver 130 includes a network connection 135. In someexamples, the network connection 135 may be a wired ethernet connectionwhich may advantageously provide low-cost, straightforward connection,and some level of security. In some examples, the network connection 135may be wireless, which may advantageously provide location flexibility.The network connection 135 may be operable to communicate to standardrouters and/or gateways and may connect to a broadband network 140(e.g., Internet). As such, the controller receiver 130 may employ anInternet connection. In some implementations, the signal processing maybe run at a remote central computer networked with the controllerreceiver 130.

As depicted in FIG. 1, the data 120 and 125 may represent RMSvibrational velocity. In some examples, to save power, data 120 and 125may be transmitted in short bursts separated by longer intervals. Thedata 120 and 125 may contain a combination of vibrations from, not onlythe target motor 105, but also adjacent motors, including those withinthe same machine or within the same mounting frame. Accordingly,vibrational disturbances may be mixed or “cross-coupled” with thevibrations of the target motor 105. Therefore, in some examples, thecontrol receiver 130, which may include a signal processor, mayimplement disturbance rejection of cross-coupled machines, effectivelyfiltering out signals unrelated to the target motor 105.

It will be explained in the proceeding figures and descriptions, how themotor on-off indication may advantageously aid in the provision ofbaseline vibrational data, ignoring vibrational data from an off motor,thereby enabling threshold calculations (during a learn mode) which, inturn, may advantageously enable triggering of diagnostic alarms ofvarious failure conditions or incipient failure conditions (during a runmode). Refer to FIG. 4 for further discussion of various failureconditions. Further, employment of the motor on-off indication as amethod to gate vibrational data (e.g., RMS velocity data 120, RMSHigh-pass acceleration data 125) from an off-motor may advantageouslyreduce false alarms.

FIG. 2A depicts a schematic view of an exemplary accelerometer dataprocessor with an event-filter enable. A data process 200A begins with aset of generated accelerometer data 205 making its way to an array ofdigital filters 210.

The array of digital filters 210 includes an RMS high-pass accelerationx-axis filter 210A, operable to produce a digital stream of overall RMSacceleration values based on the set of sampled x-axis accelerometerdata 205, over a predetermined period of time. The set of sampledaccelerometer data 205 may be digitally filtered via a digital signalprocessor (DSP) for a predetermined frequency band that may beconsidered “high frequency” in this application.

The array of digital filters 210 also includes an RMS high-passacceleration z-axis filter 210B, operable to produce a digital stream ofoverall RMS acceleration values based on the set of sampled z-axisaccelerometer data 205, over a predetermined period of time. The set ofsampled accelerometer data 205 may be digitally filtered via a digitalsignal processor (DSP) for a predetermined frequency band that may beconsidered “high frequency” in this application. For example, thefrequency band of 1-4 KHz may be considered high-pass. Other frequencybands may also be included in the definition of high-pass, based on theneeds of the application.

The array of digital filters 210 also includes an RMS velocity x-axisfilter 210C, operable to produce a digital stream of overall RMSvelocity values based on the set of sampled x-axis accelerometer data205, over a predetermined period of time. The set of sampledaccelerometer data 205 may be digitally filtered via a digital signalprocessor (DSP) for a predetermined frequency band, then digitallyintegrated to produce velocity.

The array of digital filters 210 also includes an RMS velocity z-axisfilter 210D, operable to produce a digital stream of overall RMSvelocity values based on the set of sampled z-axis accelerometer data205, over a predetermined period of time. The set of sampledaccelerometer data 205 may be digitally filtered via a digital signalprocessor (DSP) for a predetermined frequency band, then digitallyintegrated to produce velocity.

The array of digital filters 210 may also include multiple other filters210(n), operable to produce various digital streams of overall RMSvalues based on the set of sampled accelerometer data 205. In someembodiments, the digital filters 210 may be operable to eliminate dataoutliers. As such, the outputs of the digital filters 210 representoverall trending parametric data 290 suitable for proceeding signalprocessing.

An RMS filtering implementation has been described. Nevertheless, itwill be understood that various filtering techniques may be employed.For example, advantageous results may be achieved if the accelerometerdata were filtered using high-pass, low-pass, band-pass, peak detectionor kurtosis.

The signal outputs from the digital filters 210 are fed into twobranches: a top “Acute Alarm” branch and a bottom “Chronic Alarm”branch. Acute alarms are intended to detect short-term failureconditions. Chronic alarms are intended to detect long-term failuretrends.

In an illustrative acute alarm example, a motor moving a conveyor on anassembly line may be operable to move crates of empty bottles. If forexample, a worker accidentally places a much heavier crate of filledbottles on the conveyor, an acute alarm may activate alerting workers ofan issue. In some examples, much larger discrepancies in load may be setwithin the VSS to catch larger issues, for example, a worker placing arefrigerator on a conveyor built for microwave ovens.

In an illustrative chronic alarm example, a factory ventilation unit maybe operable to move fresh air into the building. Over several weeks, forexample, a bearing within the motor driving the ventilation unit mayslowly degrade generating vibrational anomalies. These anomalies mayactivate a chronic alarm, alerting factory personnel of an incipientmotor failure within the ventilation unit. Maintenance personnel mayproactively order replacement parts and repair the ventilation unit inadvance of the mechanical failure.

Accordingly, various types of acute and chronic alarms may be generatedby implementation of the depicted diagram, by customizing the acutethreshold, the chronic threshold, and other variables including M, N, P,and Q.

Continuing the figure description, the signal outputs from the digitalfilters 210 are fed into a comparator 215, via a switch SW1. The switchSW1 may be controlled by a VSS computer processor. The processor mayselect, via the switch SW1, which particular signal output from thedigital filters 210 will be monitored for various alarm conditions. Insome examples, the switch SW1 may be implemented within the processor,and may not be an actual switch, but may be implemented by reading aselected register. Further, each of the signal outputs from the digitalfilters 210, may be monitored one at a time in a round-robin fashion, byimplementation of the switch SW1 function, thereby providingtime-division multiplexing of alarm monitoring.

The switch SW1 feeds the comparator 215 is operable to determine anacute alarm condition based on a customizable acute threshold 220. Theoutput of the comparator 215, indicating an alarm condition when theoutput of the selected digital 210 crosses the threshold 220, is fedinto a digital filter 225. In the depicted example, the digital filter225 produces a true output when N input events occur within a period M.In various examples, the variables N and M may be customizable by theend user, or may remain at default values. The output of the digitalfilter 225 is defined as an acute alarm 230. This is discussed inexemplary form in the section labeled “Acute and Chronic Alarms” below.

The signal output from the digital filter 210 is also fed into acomparator 240 via switch SW2. The switch SW2 provides a parallelfunction as described above for the switch SW1. The comparator 240 isoperable to determine a chronic alarm condition based on a customizablechronic threshold 245. The output of the comparator 240, indicating analarm condition when the output of the selected digital filter 210crosses the threshold 245, is fed into a digital filter 250. In thedepicted example, the digital filter 250 produces a true output when Pinput events occur within a period Q. In various examples, the variablesP and Q may be customizable by the end user, or may remain at defaultvalues. The output of the digital filter 250 is defined as a chronicalarm 255. Again, this is discussed in exemplary form in the sectionlabeled “Acute and Chronic Alarms” below.

In an illustrative “learn mode” example, a sensor mounted to a monitoredmotor produces overall trending parametric data 290, for example, RMSHigh-Pass acceleration data (x-axis and z-axis) 210A and 210B, RMSvelocity data (x-axis and z-axis) 210C and 210D, and other trendingparametric data, for the motor which may be turned on and off during itsnormal operation. Overall trending parametric data 290 during themotor-off time is not to be acquired. In some examples, the overalltrending parametric data 290 may be acquired but may not be used inthreshold calculations. Acquiring or utilizing the data during themotor-off time may result in inaccurate baseline measurements which mayresult in inaccurate alarm thresholds. In “run-mode”, acquiring dataduring motor-off time may result in triggering alarms based onirrelevant data. Therefore, a motor on-off circuit 260 fed by thedigital filters 210 is included in the data process 200A. The motoron-off circuit 260 produces a Motor-ON logic signal 265. The Motor-ONlogic signal 265 drives an enable function 270. The enable function 270is employed as a gate for a filtering/detection block 275. Variousembodiments of the motor on-off circuit 260 are described further inFIGS. 3A and 3B.

If the Motor-ON logic signal 265 is true, then the processes within thefiltering/detection block 275 are actively processing, and the signalscoming from the digital filters 210 are considered valid for use. If theMotor-ON logic signal 265 is false, then the processes within thefiltering/detection block 275 are prevented from triggering alarms 230and 255.

In various implementations, signals entering the comparators 215 and240, respectively labeled La1 and Lc1, are points used to collect dataduring a learning mode. The VSS employs the learning mode to collectfiltered vibrational data while the motor is running. This collecteddata is used to “baseline” normal running vibrations. The baselines maybe used to calculate various alarm thresholds (for example, acute andchronic thresholds 220 and 245). During the learning mode, the Motor-ONlogic signal 265 connected to the enable function 270 is employed tocontrol the baseline operation. Only data collected during the activeenable function 270 may be used to calculate the vibrational runningbaseline.

In some examples, multiple processors, or multiple software threads maybe employed to monitor one or more of the signal outputs from thedigital filters 210 simultaneously. Further, multiple blocks 275 may beemployed within the data process 200A, each implementing one position ofthe switches SW1 and SW2, as exemplified in FIGS. 5A, 5B, 5C and 5D.Such parallel monitoring may provide a comprehensive monitoring of allthe different output alarm signals.

FIG. 2B depicts a schematic view of an exemplary accelerometer dataprocessor with a data-filter enable. A data process 200B begins with theaccelerometer data 205 making its way to the digital filters 210. Insome embodiments, the digital filters 210 are operable to eliminate dataoutliers. As such, the outputs of the digital filters 210 may F

As discussed in FIG. 2A, the signal outputs from the digital filters 210are fed into two branches; a top “Acute Alarm” branch and a bottom“Chronic Alarm” branch. Accordingly, various types of acute and chronicalarms may be generated by implementation of the depicted diagram, bycustomizing the acute threshold, the chronic threshold, and othervariables including N, and P.

The signal outputs from the digital filters 210 are fed into an N-samplemoving average filter 280, via the switch SW1. The switch SW1 may becontrolled by a VSS computer processor. The processor may select, viathe switch SW1, which particular signal output from the digital filters210 will be monitored for various alarm conditions. In some examples,the switch SW1 may be implemented within the processor, and may not bean actual switch, but may be implemented by reading a selected register.Further, each of the signal outputs from the digital filters 210, may bemonitored one at a time in a round-robin fashion, by implementation ofthe switch SW1 function, thereby providing time-division multiplexing ofalarm monitoring.

The switch SW1 feeds the N-sample moving average filter 280. In variousexamples, the variable N may be customizable by the end user, or mayremain at a default value. The output of the moving average filter 280feeds a comparator 215B. The comparator 215B is operable to determine anacute alarm condition based on a customizable acute threshold 220B. Theoutput of the comparator 215B is defined as an acute alarm 230B.

The signal outputs from the digital filters 210 are also fed into aP-sample moving average filter 285 via the switch SW2. The switch SW2provides a parallel function as described above for the switch SW1. Invarious examples, the variable P may be customizable by the end user, ormay remain at a default value. The output of the moving average filter285 feeds a comparator 240B. The comparator 240B is operable todetermine a chronic alarm condition based on a customizable chronicthreshold 245B. The output of the comparator 240B is defined as achronic alarm 255B.

In an illustrative “learn mode” example, a sensor mounted to a monitoredmotor produces overall trending parametric data 290, for example, RMSHigh-Pass acceleration data (x-axis and z-axis) 210A and 210B, RMSvelocity data (x-axis and z-axis) 210C and 210D, and other trendingparametric data, for the motor which may be turned on and off during itsnormal operation. Overall trending parametric data 290 during themotor-off time is not to be acquired. In some examples, the overalltrending parametric data 290 may be acquired but may not be used inthreshold calculations. In these examples, acquisition of the overalltrending parametric data 290 may be recorded and may be employed forvarious purposes, for example, to determine differences between therunning and not running motor states. Acquiring or utilizing the dataduring the motor-off time may result in inaccurate baseline measurementswhich may result in inaccurate alarm thresholds. In “run-mode”,acquiring data during motor-off time may result in triggering alarmsbased on irrelevant data. Therefore, the motor on-off circuit 260 fed bythe digital filters 210 is included in the data process 200B. The motoron-off circuit 260 produces the Motor-ON logic signal 265. The Motor-ONlogic signal 265 drives an enable function 270B. The enable function270B is employed as a gate for a filtering/detection block 275B. Variousembodiments of the motor on-off circuit 260 are described further inFIGS. 3A and 3B.

If the Motor-ON logic signal 265 is true, then the processes within thefiltering/detection block 275B are actively processing, and the signalscoming from the digital filters 210 are considered valid for use.Accordingly, the moving average is calculated on the data.

If the Motor-ON logic signal 265 is false, then the data samples are notfolded into the moving average. Worded another way, the moving averagefreezes when the motor is not running and last data value is held untilthe motor resumes running. Accordingly, the processes within thefiltering/detection block 275B are prevented from triggering alarms 230Band 255B.

In this way, an accurate filtered version of the data set is producedwhen the motor is running. This data set may be compared to the alarmingthresholds to create accurate overall “check engine light” events.

In various implementations, signals exiting the moving average filters280 and 285, respectively labeled La2 and Lc2, are points used tocollect data during a learning mode. The VSS employs the learning modeto collect filtered vibrational data while the motor is running. Thiscollected data is used to “baseline” normal running vibrations. Thebaselines are in turn used to calculate various alarm thresholds (forexample, acute and chronic thresholds 220B and 245B). During thelearning mode, the Motor-ON logic signal 265 connected to the enablefunction 270B is employed to control the baseline operation. Only datacollected during the active enable function 270B is used to calculatethe vibrational running baseline.

A moving average filtering implementation has been described.Nevertheless, it will be understood that various filtering techniquesmay be employed. For example, advantageous results may be achieved ifthe overall trending parametric data 290 were filtered using a low-passfilter, weighted moving average, gaussian, window, multiple-pass movingaverage or various smoothing filters.

In some examples, multiple processors, or multiple software threads maybe employed to monitor one or more of the signal outputs from thedigital filters 210 simultaneously. Further, multiple blocks 275 may beemployed within the data process 200B, each implementing one position ofthe switches SW1 and SW2, as exemplified in FIGS. 6A, 6B, 6C and 6D.Such parallel monitoring may provide a comprehensive monitoring of allthe different output alarm signals.

FIG. 3A depicts a schematic view of an exemplary motor on-offdetermination circuit. In FIG. 3A, the motor on-off circuit 260introduced in FIGS. 2A and 2B is further detailed in an exemplaryembodiment 260A. In this embodiment, the Motor-ON logic signal 265 willbe active when ANY of the following events occur:

a) when the RMS high-pass acceleration in the x-axis crosses a threshold310.

b) when the RMS high-pass acceleration in the z-axis crosses a threshold320.

c) when the RMS velocity in the x-axis crosses a threshold 330.

d) when the RMS velocity in the z-axis crosses a threshold 340.

In each case the thresholds 310, 320, 330, and 340 may be pre-determinedthresholds. A system processor within a controller (e.g., FIG. 1, item130) may control a gating enable signal for each threshold crossingevent. The enables (depicted as Enable A, Enable B, Enable C, and EnableD) provides flexibility by allowing the system to choose which overalltrending parametric data 290 to employ for generating the Motor-ON logicsignal 265.

FIG. 3B depicts a schematic view of an exemplary motor on-offdetermination circuit. In FIG. 3B, the motor on-off circuit 260introduced in FIGS. 2A and 2B is further detailed in an exemplaryembodiment 260B. In this embodiment, the Motor-ON logic signal 265 willbe active when ALL of the following events occur:

a) when the RMS high-pass acceleration in the x-axis crosses thethreshold 310.

b) when the RMS high-pass acceleration in the z-axis crosses thethreshold 320.

c) when the RMS velocity in the x-axis crosses the threshold 330.

d) when the RMS velocity in the z-axis crosses the threshold 340.

With reference back to FIG. 1, the VSS includes the sensor 110 coupledto the mechanical drive unit, which may include a various motor types.The sensor 110 is operable to produce vibrational data which mayinclude, for example, RMS velocity data 120 and RMS high-passacceleration data 125. The vibrational data sent from the sensor 110 mayinclude an array data referred to as overall trending parametric data(FIG. 2, item 290). FIG. 3A depicts a schematic view of an exemplarymethod to produce the Motor-ON logic signal 265 exemplified in FIGS. 2Aand 2B from the overall trending parametric data (FIG. 2, item 290). Asexplained in FIGS. 2A and 2B, the motor on-off signal (Motor-ON logicsignal 265) may provide a gating signal to produce a long-term movingaverage which may advantageously be used to calculate accuratevibrational baselines, standard deviations, and other statisticalmeasures from which alarm thresholds may be generated, and nuisancealarms may be avoided.

The overall RMS high-pass acceleration may be employed as a criterionfor motor on-off in many applications. RMS high-pass accelerationcouples very inefficiently across connected machines and motors withinthe same machine or frame, so it tends to be isolated to the machine ormotor of interest. Accordingly, an absolute threshold criterion may beemployed to predict motor on-off for some machines or motors. In someexamples, absolute thresholds of about 5 mG, 10 mG, 15 mG, 20 mG, 25 mG,30 mG, 35 mg, 40 mG, 45 mG or about 50 mG may be employed. The unit “mG”stands for “milli-gravity” which is a scale of accelerationapproximately equal to 9.8 mm/sec²

A justification for an on-off threshold is presented in the followingillustrative example. An intrinsic noise level for the sensor 110 may bevery stable at about 10 mG. In such examples, the accelerationthresholds 310 and 320, for motor on-off, may be set at twice theintrinsic noise level, or 20 mG. In contrast, and in some examples,rotating mechanical drive units, when on, may produce more than 30 mG ofsteady-state RMS high-pass acceleration, thereby producing a trueMotor-ON indication, well above the intrinsic noise level and above theacceleration threshold.

In some applications, some machines/motors may produce very littlehigh-frequency acceleration signal. Therefore, in some embodiments, acombination of multiple axes and/or multiple variables may be employed,which may advantageously produce a positive on-off signal, due tomonitoring of more than one axis.

In some embodiments, multiple parameters may be logically “OR-ed.” Forexample, as depicted in FIG. 3A, in the current context with theparameters available, RMS velocity may be logically “OR-ed” with RMShigh-pass acceleration. Because multiple parameters are employed in thisapproach, each individual on/off threshold may be set higher than ifeither parameter were considered independently. The higher individualon/off threshold may advantageously provide more margin for falsepositives on any given variable.

In an illustrative example, the RMS high-pass acceleration Motor-ONthreshold 310 and 320 may be about 5 mG, 10 mG, 15 mG, 20 mG, 25 mG, 30mG, 35 mg, 40 mG, 45 mG or about 50 mG. In a related illustrativeexample, the overall RMS velocity Motor-ON thresholds 330 and 340 may beabout 0.05 in/sec. These values may determine motor on-off conditionsacross a variety of machines and scenarios.

In some embodiments, the motor on-off signal (Motor-ON logic signal 265)may be an on-off flag. The on-off flag may be determined in the actualsensor transmitter, instead of the controller receiver. In suchembodiments, the sensor may determine the running state of the coupledtarget motor. Further, in various examples, the sensor may employ any ofthe methods described in this application referring to the generation ofthe motor on-off signal (Motor-ON logic signal 265) to determine theon-off flag.

In some implementations, the on-off flag may be placed in the leastsignificant bit (LSB) or most significant bit (MSB) of one of the bytevariables pushed out of the sensor's communication protocol. Forexample, one unused bit within the temperature variable may be employedfor the on-off flag. In some implementations, a dedicated byte variablemay be employed to hold the on-off flag.

In some examples, motor on-off detection may be accomplished by theselection of parameters and thresholds that both reliably indicate whenthe motor is running, and applies to a wide range of machines/motorswithout the user needing to “tune” thresholds for their individualmachines/motors per installation. Accordingly, in some embodiments, aglobal absolute set of motor on-off criteria may be employed. Further,the employment of the RMS high-pass acceleration as the on-off criterionmay provide that reliable indication.

In some embodiments, the RMS high-pass acceleration (FIGS. 2A and 2B,items 210A and 210B) may be monitored during a learn mode. One of thepurposes of the learn mode may be to set the RMS high-pass accelerationthresholds (e.g., FIGS. 3A and 3B, items 310, 320, 330 and 340). In suchembodiments, the thresholds (e.g., FIGS. 3A and 3B, items 310, 320, 330and 340) may be set at a level between the intrinsic noise of the sensorand the average peak level of the RMS high-pass acceleration. Anexemplary waveform captured during the learn mode is shown in the dataFIG. 1 item 125. It should be noted that setting an on-off threshold forthe RMS high-pass signal (as exemplified by the motor on-off indicationsin FIG. 1 above the graphed data 125), may be more straight-forward thansetting an on-off threshold from the RMS velocity as exemplified in thesensor data (FIG. 1, item 120). The exemplary sensor data 120representing RMS velocity shows instances where some ON indications arelower than some OFF indications (as shown just below the graphed data120). Accordingly, in the RMS velocity data (FIG. 1, item 125) there isno one-static-threshold above which is on, and below which is OFF. Incontrast, by implementation of the Hi-Frequency Acceleration methodsdescribed in this document, the on-off indications (FIG. 1 above thedata 125), clearly possess a much higher degree of distinction betweenthe levels indicating ON and the levels indicating OFF.

FIG. 4 depicts a block diagram of a binning scheme and failure diagnosisbased on frequency, of an exemplary VSS data processing algorithm. A VSSdata processing algorithm 400 includes the sensor 110. The sensor 110includes an accelerometer 405. The time-domain data stream from theaccelerometer 405 is processed by a processor internal to the sensor110. The processor is operable to produce the array of overall trendingparametric data 290 which are output from the sensor 110. In someexamples, a user site may couple the output from the sensor 110 directlyto the controller 130 via a hardwire connection 410B. In some examples,a user site may couple the output from the sensor 110 to the transmitternode 115 via a hardware connection 410B which may be operable to sendthe Overall Trending Parameters wirelessly to the controller 130 via awireless channel 410C.

In an illustrative example, the overall trending parametric data 290 maybe RMS frequency-domain data, filtered and converted from thetime-domain data from the accelerometer 405, and may correspond to amonitored factory machine/motor. In some examples, one or moremachines/motors may be monitored in a single installation by multiplesensors 110 coupled to a single controller 130. The controller 130 maybe operable to process the overall trending parametric data 290.

The output of the accelerometer 405 is operatively coupled to aband-pass filter 415. In the depicted example, the band is 1 KHz to 4KHz. In some examples, the 1 KHz to 4 KHz band may represent RMSHigh-Pass Acceleration. The output signal produced from the 1 KHz to 4KHz band-pass filter 415 may be compared to a threshold, as described inFIG. 3A and FIG. 3B, to produce a Motor-ON logic signal, as employed inFIGS. 2A and 2B.

The output of the accelerometer 405 is operatively coupled to aband-pass filter 425. In the depicted example, the band is 0.01 KHz to1.00 KHz. In some examples, the 0.01 KHz to 1.00 KHz band may representRMS Velocity. The output signal produced from the 0.01 KHz to 1.00 KHzband-pass filter 425 may be compared to a threshold, as described inFIGS. 2A and 2B, and may produce the acute alarm 230 and 230B or thechronic alarm 255 and 255B. These alarms generated from the RMS velocitymay indicate a general motor failure state of a target motor.

The output of the accelerometer 405 is operatively coupled to aband-pass filter 430. In the depicted example, the band surrounds amotor fundamental frequency by about 20%. In some examples, 20-40 Hz mayrepresent the motor fundamental frequency. In some examples, 50-70 Hzmay represent the motor fundamental frequency. In various examples, theband surrounding the motor fundamental frequency may represent a loosemotor state of a target motor. The output signal produced may becompared to a threshold, as described in FIGS. 2A and 2B, and producethe acute alarm 230 and 230B or the chronic alarm 255 and 255B.

The output of the accelerometer 405 is operatively coupled to aband-pass filter 435. In the depicted example, the band surrounds twicethe motor fundamental frequency by about 20%. In some examples, 45-130Hz may represent twice the motor fundamental frequency. In variousexamples, the band surrounding twice the motor fundamental frequency mayrepresent an axial misalignment state of a target motor. The outputsignal produced from the band-pass filter 435 may be compared to athreshold, as described in FIGS. 2A and 2B, and produce the acute alarm230 and 230B or the chronic alarm 255 and 255B.

Although various embodiments have been described with reference to thefigures, other embodiments are possible. For example, in someembodiments, a sensor may directly connect to an ethernet port, actingas if it were a computing device within a facility's computer network.In some examples, a single sensor system may be wireless, and mayconnect to a facility's Wi-Fi network. In some embodiments, instead ofconnection to a local facility network, the VSS may connect to anexternal network such as a cellular data network. Accordingly, allreferences in this document relating to connection to the Internet, mayemploy a cellular data link.

Continuous Vibration Monitoring

In some embodiments, the VSS may monitor one or more mechanical driveunits. A sensor may be placed upon each mechanical drive unit to sensevelocity and acceleration, and may be converted within the sensor to RMSvelocity and RMS high-pass acceleration. Each sensor may be connected toa dedicated transmitter node. In some examples, one or more sensors maybe connected to a single transmitter node. RMS velocity may beindicative of general rotating machine/motor health (e.g., unbalance,misalignment, looseness). High-frequency (high-pass) acceleration may beindicative of early bearing wear.

Self-Learning Baseline and Thresholds

In some embodiments, the VSS may employ machine-learning algorithms tocreate an initial baseline reading and warning/alarm thresholds for eachmechanical drive unit, individually. In such embodiments, thismachine-learning may advantageously free users from generating baselinesor alarms.

In an illustrative example, each target motor may have its ownvibrational characteristics and therefore unique baseline measurementsmay be acquired. In some examples, the machine-learning algorithms maygenerate that baseline for the target motor vibration characteristics(RMS velocity and RMS high-frequency acceleration for X and Z axis). Insome examples, the VSS may use the baseline to generate warnings andalarms based on statistical analysis, for example, using the standarddeviation from the mean, for example, two standard deviations (2 sigma)outside of the mean for warnings, and three standard deviations (3sigma) outside of the mean for alarms.

The VSS may create a filtered baseline during normal running operationand may also calculate the variance of the dataset collected during thisphase, so that the system may determine its own alarming threshold(s).From the baseline and the variance, the system may calculate thresholdsas a multiple of the standard deviation of the “training” dataset.Alternatively, the system may calculate a threshold as a multiple of, ora percentage of, the actual baseline itself. During run-time, the systemmay compare the measured data to these stored thresholds to generatealarms.

In some embodiments, “baseline+deviations” alarming thresholds may beemployed. Accordingly, a running motor may be monitored for a period oftime to collect a baseline data set. To calculate baseline anddeviations, the motor on-off state may be employed to gate datacollection. Further, statistics may be calculated from this data set.Once motor-running data is collected from a substantial time period(e.g., 24 hours) of actual running time, parameters may be calculated.

The baseline for a particular parameter may be the calculatedstatistical mean average of the collected data set. Further, variance ofthe collected data set may be calculated. Accordingly, the standarddeviation of the collected data set may be determined for the particularparameter. Further, alarm thresholds may be generated from thestatistics. In an illustrative example, a default “warning” level may bebaseline plus 2 times the standard deviation. Further, for example, adefault “alarming” level may be baseline plus 3 times the standarddeviation. During run-time, the measured data set may be filtered andcompared to these threshold levels to generate actionable alarms.

In some embodiments, baseline and alarm levels may be overridden by auser. In such examples, the user may manually enter custom thresholdlevels via a user interface. In some examples, the threshold may referto a range (an upper and lower threshold pair) such that the targetmotor vibrational data must exist “between” the thresholds to beconsidered normal. Further, vibrational data outside the limits, eitherabove the upper limit or below the lower limit, may be considered analarm condition. In some examples of threshold range conditions, theupper threshold may be positive and the lower limit may be negative,such that “no vibration” or “zero vibration” is within range, andconsidered a non-alarm condition. In some examples, both the high andlow limits may be positive. In some examples, both the high and lowlimits may be negative.

Learn Mode

In some embodiments, the VSS may employ various methods for learning avibrational baseline, to establish the threshold to determine motoron-off. While operating a machine in steady-state, the VSS may beimplemented by:

-   -   a. Providing an accelerometer coupled to a body of a motor        within a machine;    -   b. Periodically sampling, from the accelerometer, an        accelerometer output signal for a predetermined sampling        interval;    -   c. During each predetermined sampling interval, applying a        predetermined frequency band filter (e.g., 1-4 kHz);    -   d. During each predetermined sampling interval, calculating the        RMS value of the resulting frequency-filtered signal;    -   e. Defining an average motor-on value from the distribution of        the resulting RMS values, the average motor-on value defined by        the statistical mean;    -   f Defining a motor-on-off threshold by calculating a        predetermined percentage (e.g., 50%) of the motor-on value.

In some embodiments, the VSS may employ a predetermined level as themotor on-off threshold (e.g., 20 mG). The predetermined level may bedetermined empirically by employment of a predetermined scale value(e.g., 2×) of the normal expected RMS noise-floor (e.g., 10 mG) of anaccelerometer.

In some embodiments, the VSS may employ various methods for learning avibrational baseline, to establish the threshold to that may determinean alarm or a warning. The VSS may be implemented by:

-   -   a. Providing an accelerometer coupled to a body of a motor        within a machine;    -   b. Operating the motor in steady-state;    -   c. Periodically sampling, from the accelerometer, an        accelerometer output signal for a predetermined sampling        interval;    -   d. During each predetermined sampling interval, applying a        predetermined High-Pass frequency band filter (e.g., 1-4 kHz);    -   e. During each predetermined sampling interval, calculating the        RMS value of the resulting frequency-filtered signal;    -   f Defining an average motor-on value from the distribution of        the resulting RMS values, the average motor-on value defined by        the statistical mean;    -   g. Defining a motor-on-off threshold by calculating a        predetermined percentage (e.g., 50%) of the average motor-on        value. Values above the threshold being defined as        MotorRunning=true. Values below the threshold being defined as        MotorRunning=false.    -   h. Operating the motor in normal running state;    -   i. Periodically sampling, from the accelerometer, a second        accelerometer output signal for a predetermined sampling        interval;    -   j. During each predetermined sampling interval, applying one or        more predetermined frequency band filters (e.g., 0.01-1 kHz,        motor fundamental, 2× motor fundamental, 1-4 kHz);    -   k. During each predetermined sampling interval, calculating an        RMS value for each of the resulting frequency-filtered signals;    -   l. Calculating a long-term running average (baseline) for each        of the resulting (e.g., RMS, scalar, or peak) values, “gating        ON” the running average when MotorRunning is true and “gating        OFF” the running average when MotorRunning is false;    -   m. For each of the resulting long-term averages, determining an        alarm threshold by calculating a predetermined percentage above        each of the resulting long-term averages (baselines).        Alternately, determining an alarm threshold by calculating        baseline plus a predetermined number of standard deviations.

Acute and Chronic Alarms

In some and embodiments, by employment of the on-off flag to gate datacollection, accurate moving average filters may be generated therebytriggering true alarm events. Moving averages in these embodiments maybe accurate due to removal of data when the on-off flag is “off” (motoris off).

In some exemplary approaches, a moving average may be applied to thedata set and may be compared to various alarm thresholds. In someexamples, the approach to generate chronic alarms may be different thanthe approach to generate acute alarms.

In various exemplary approaches, a filter applying a “percentageexceeding alarm per time” variation may be employed. In some examples,one variation may be to require “M” samples exceeding threshold every“N” samples of motor running condition. For example, the filter mayrequire 80 of 100 samples. In an illustrative example, if the values areabove a predetermined threshold for 80% of the time (or samples) in a10-minute period, then an acute alarm may be triggered. If the keyvalues are above a predetermined threshold for 80% of the time (orsamples) in a 16-hour period, then a chronic alarm may be triggered.Accordingly, using predetermined time-periods may allow differentiationbetween one or more acute alarm conditions and/or one or more chronicalarm conditions. For example, a “step increase in load or jam” may bedistinguished from a chronic machine/motor failure. Also, for example,worn bearings may be distinguished from a loose or unbalanced condition.In such examples, the “percentage exceeding alarm per time” may beemployed, particularly in situations with frequent on/off cycles, due tostarting and stopping periods where the mechanical drive unit may not beat peak power or peak load. Accordingly, even though the steady-statevalues may be above an on/off threshold, the transition values, whilestill above the non-running state, may not be representative of therunning condition. Two or more predetermined acute conditions, which maypresent simultaneously or in a prescribed sequence, may, in someimplementations, also trigger a predetermined chronic alarm condition.

In some embodiments, a “check engine light” alarm and warning may begenerated for both acute and chronic conditions for each motor. Acutethresholds may indicate a short-term condition such as a jammed motor ora motor stall. These conditions may cross the threshold rapidly. In someexamples, chronic thresholds may use a multi-hour moving average of thevibration signals to indicate a long-term condition such as a wearing orfailing bearing or failing motor.

Motor Run-Time

In some embodiments, the VSS may recognize and differentiaterunning-state versus idle-state to track cumulative run-time for eachmotor.

Temperature Alarms

In some examples, the vibration sensor for a VSS may include atemperature monitor. The temperature and monitor may alarm when apredetermined threshold is exceeded.

Remote Master Light

In some examples, the VSS output may be employed as an input to gatelighting on various tower lights. Accordingly, a master tower light maybe employed to indicate an “OR-ed” state of one or more vibrationalarms.

SMS Text/Email Alerts

In some embodiments, the VSS may generate SMS texts and/or email alertsbased on individual warnings and/or alarms.

Cloud Monitorin!

Some embodiments may push data to a cloud Web server or to aprogrammable logic controller (PLC) via a local area network (LAN) orcellular connection for remote viewing, alerting, and logging.

Exemplary Installation

In an illustrative implementation, a user mounts sensors and nodes tomachines and/or motors of interest and activates a VSS vibrationsolution in a controller. Next, the user actuates the machine-learningsequence. In some examples, the learning sequence may be about 24 hoursof motor-on operation. At the end of the machine-learning, the baselineand standard deviation information may be stored in system memory.Further, alarming thresholds may be calculated for the variables ofinterest.

Implementation of Alarms

The system may generate both acute and chronic alarm thresholds. Theacute and chronic alarm thresholds may each include both warning andsevere (high) levels for two sensor axes, for both RMS velocity, and RMShigh-frequency acceleration.

After the machine-learning sequence, the system may enter the run-timemode during which time the data set from the sensors is compared againstmultiple alarm thresholds described above. Various alarms may betriggered based on the data set, or a processed data set, exceedingvarious alarm thresholds.

In some implementations, alarms may be indicated in data registers. Theregisters may be pushed to the cloud, stored on a nonvolatile memorydevice, or sent to a user's system controller for further evaluation orprocessing.

In various implementations, alarms may be mapped to one or more physicaloutputs on the controller. Alarms may also be routed through a remoteradio to trigger audible or visual indicators.

In various implementations, two or more alarms may be “OR-ed” together,such that a single composite alarm may be implemented. In suchimplementations, a user may employ the controller GUI to determine whichspecific alarm(s) and/or machine variable(s) triggered the compositealarm.

In some embodiments, the “VSS generated Motor-ON logic signal” asdescribed in this document, may be used to calculate a “running hours”tabulation. In some implementations, the Motor-ON logic signal may beused to count “motor on/off cycles.” Users may find benefit in the“running hours” tabulation and/or the “motor on/off cycles” count, incalculating machine productivity and/or in calculating excessive cyclingfor determining running smoothness.

Other Vibrational Sensing Methods

In some implementations, the VSS may employ other methods of sensingvibration. For example, a laser may be employed as a position sensor.Various derivatives may be calculated from the transient positional data(e.g., to produce velocity, acceleration, or jerk). In some examples,piezo film may sense relative movement. Also, various MEMS sensors maybe employed to sense position, velocity and/or acceleration.

Other Sensing Modalities

In some embodiments, sensors within the VSS may include a temperaturesensor. The temperature sensor may be monitored and compared to a fixedtemperature alarm threshold. In some examples, the temperature alarmthreshold may be user adjustable. Temperatures exceeding the thresholdmay generate high-temperature alarms. Users may find benefit in thetemperature alarms in determining, for example, coincident temperaturerises with motor maintenance or with motor state changes.

Mounting of the Sensor

In some embodiments, manufacturers' prescribed mounting of the VSSsensor may provide for substantially accurate readings. A user mayinstall the VSS sensor rigidly to a target motor, and close to thebearing of the target motor. The VSS vibrational sensors may have one ormore axes of acceleration sensing. In some embodiments, a z-axis may benormal to the plane of the sensor. In some examples, an x-axis may behorizontal to the sensor. In further examples, a y-axis may be verticalto the sensor. In an illustrative example, a user may install the sensorsuch that the x-axis is parallel with a target motor shaft. Accordingly,a user may install the sensor such that the z-axis is radial to thetarget motor shaft. In some examples, sensing may take place on thex-axis and z-axis. In some examples, sensing may take place on they-axis.

Various exemplary methods may be employed to couple the VSS sensor to atarget motor. For example, thermally conductive adhesive tape may beapplied between the VSS sensor and the target motor. Installers may findbenefit with this method due to its ease-of-use. In various examples, aflat magnet sensor bracket may be employed, in such examples, thebracket may provide a strong solid and adjustable mount to the targetmotor. In some examples, a curved surface magnet coupled to a sensorbracket may be employed to couple the VSS sensor to the target motor. Inthese implementations, the curved surface magnet may provide a strongmount to small curved motor surfaces. Installers may find benefit withthe curved surface magnet due to its strong coupling force andease-of-installation.

Various Embodiments

In some examples, various modification may be made to the described VSSsystems. Accordingly, advantageous results may be achieved if the VSScomponents of the disclosed system were combined in a different manner.For example, various embodiments may combine the sensor and thetransmitter. In some examples, the wireless aspects may be eliminated,and the sensor may be directly connected to the controller receiver. Insome examples, a VSS may be a single sensor system, including only asensor, which may incorporate all the functionality of the senor and thecontroller.

In some examples, the term “motor” may refer to various mechanical driveunits. For example, in some implementations, the VSS may be employed tomonitor rotating motors, vibrational motors, and articulating motorssuch as windshield wipers. In some examples, the VSS may monitorelectric motors. In some examples, the VSS may monitor internalcombustion engines. In various examples, the VSS may be employed tomonitor the bearings on a computer hard drive. Some embodiments maymonitor one or more vibration characteristics. For example, monitoringmay be employed on the X and Z axis for one or more motors.

In some examples, the VSS may provide wireless or wired access to apersonal computer (e.g., laptop, tablet, cell phone) such that controlnormally facilitated through use of the controller receiver may insteadoccur on the personal computer. Users may find benefit in leveraging thedisplay screen and/or other user interface elements (e.g., mouse) on thepersonal computer, to provide interaction with the VSS. In variousexamples, the personal computer may augment the functionality of theVSS, such that the user may be provided a larger and more colorful GUIand more user-friendly input devices than on the controller. In suchembodiments, the controller may continue to provide its specifiedfunctions, but may in addition provide more user interaction. In someembodiments, the personal computer may act as a setup and/or aprogramming tool to the controller. In some embodiments, the personalcomputer may replace the controller.

In some exemplary descriptions, the term “demodulation” may be definedas running analytics on sampled data to determine various aspects, forexample, to determine when the motor is running. Further, comparingrun-time vibrational data from the motor, to various thresholds, may beindicative of trouble, and may trigger various alarms.

In some examples, known machine/motor wear conditions (e.g., unbalance,misalignment, looseness, bearing damage) may be identified by measuringacceleration time waveforms and from these waveforms calculating andmonitoring the overall RMS velocity at mid-range frequencies (e.g.,10-1000 Hz) and the overall RMS acceleration at higher frequencies(e.g., >1000 Hz).

In some embodiments, a site survey feature may be employed within theVSS. The site survey may allow the user to select each data transmitter(e.g., transmitter node 115) within the system to verify the wirelessconnection between the data transmitter and the controller receiver.

In an illustrative example, continuous monitoring of vibration data mayminimize downtime, allow tracking of uptime, and may send alerts toappropriate personnel of the need of maintenance. Such advanced warningof needed maintenance may avoid costly machine failures.

In some embodiments, the VSS may create a “check engine light” for eachmotor using in-depth analysis to indicate unusual characteristicswithout user interaction. This approach may save time and money bylimiting full-spectrum vibration analyses to only when a motor isindicating a potential failure mode. This targeted approach may avoidunexpected downtime of motors by recognizing a potential failure earlyon, giving time to schedule preventative maintenance and extend machinelife.

In various examples, the user may not need to adjust any parameterswithin the VSS. The parameters may be “learned” by the VSS, or may bepreprogrammed absolute default levels. Accordingly, the user may only beexpected to initiate a “learning” sequence during which the machine runsunder normal conditions and the system collects data.

Various embodiments may achieve one or more advantages. For example,factory managers may be informed when a motor is running or not-running.In some examples, process managers, or other personal may monitor alarge number of machines/motors at once for the running or not-runningindication. In some examples, the VSS may autonomously determine if amotor is on or idle based on vibrations in a machine that containsmultiple sources of vibration. In some examples, the VSS may evaluatebaseline data and determine when the motor is running, which mayadvantageously create accurate baselines and standard deviations fromwhich the alarm thresholds may be generated.

In some embodiments, the VSS may reliably implement disturbancerejection of cross-coupled machines. For example, vibrations from nearbymotors within the same mechanical housing or frame may be substantiallyfiltered out (rejected) from the vibrations of the target motor.

In some embodiments, the VSS may reliably predict and generate alarmsfor acute events such as machine jams and stalls. In some examples, theVSS may implement a straight-forward machine-learning approach that mayrequire a minimum of user interaction and parameter selection to setup.

In various examples, the comparison functions may be digital logiccircuits, for example, implemented with logic gates. In some examples,the comparison functions may be analog circuitry, for example,implemented with comparators and/or transistors. Further, the comparisonfunctions may be implemented in circuitry with a combination of digitaland analog components. In some embodiments, the comparator circuits mayemploy hysteresis. In yet other examples, the logic functions may beimplemented in a computer processor.

Computer System

Some aspects of embodiments may be implemented as a computer system. Forexample, various implementations may include digital and/or analogcircuitry, computer hardware, firmware, software, or combinationsthereof. Apparatus elements can be implemented in a computer programproduct tangibly embodied in an information carrier, e.g., in amachine-readable storage device, for execution by a programmableprocessor; and methods can be performed by a programmable processorexecuting a program of instructions to perform functions of variousembodiments by operating on input data and generating an output. Someembodiments may be implemented advantageously in one or more computerprograms that are executable on a programmable system including at leastone programmable processor coupled to receive data and instructionsfrom, and to transmit data and instructions to, a data storage system,at least one input device, and/or at least one output device. A computerprogram is a set of instructions that can be used, directly orindirectly, in a computer to perform a certain activity or bring about acertain result. A computer program can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example and not limitation, both general and specialpurpose microprocessors, which may include a single processor or one ofmultiple processors of any kind of computer. Generally, a processor willreceive instructions and data from a read-only memory or a random-accessmemory or both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including, by way of example, semiconductor memory devices, such asEPROM, EEPROM, and flash memory devices; magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; and,CD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, ASICs (application-specificintegrated circuits). In some embodiments, the processor and the membercan be supplemented by, or incorporated in hardware programmabledevices, such as FPGAs, for example.

In some implementations, each system may be programmed with the same orsimilar information and/or initialized with substantially identicalinformation stored in volatile and/or non-volatile memory. For example,one data interface may be configured to perform auto configuration, autodownload, and/or auto update functions when coupled to an appropriatehost device, such as a desktop computer or a server.

In some implementations, one or more user-interface features may becustom configured to perform specific functions. An exemplary embodimentmay be implemented in a computer system that includes a graphical userinterface and/or an Internet browser. To provide for interaction with auser, some implementations may be implemented on a computer having adisplay device, such as an LCD (liquid crystal display) monitor fordisplaying information to the user, a keyboard, and a pointing device,such as a mouse or a trackball by which the user can provide input tothe computer.

In various implementations, the system may communicate using suitablecommunication methods, equipment, and techniques. For example, thesystem may communicate with compatible devices (e.g., devices capable oftransferring data to and/or from the system) using point-to-pointcommunication in which a message is transported directly from the sourceto the receiver over a dedicated physical link (e.g., fiber optic link,infrared link, ultrasonic link, point-to-point wiring, daisy-chain). Thecomponents of the system may exchange information by any form or mediumof analog or digital data communication, including packet-based messageson a communication network. Examples of communication networks include,e.g., a LAN (local area network), a WAN (wide area network), MAN(metropolitan area network), wireless and/or optical networks, and thecomputers and networks forming the Internet. Other implementations maytransport messages by broadcasting to all or substantially all devicesthat are coupled together by a communication network, for example, byusing omni-directional radio frequency (RF) signals. Still otherimplementations may transport messages characterized by highdirectivity, such as RF signals transmitted using directional (i.e.,narrow beam) antennas or infrared signals that may optionally be usedwith focusing optics. Still other implementations are possible usingappropriate interfaces and protocols such as, by way of example and notintended to be limiting, USB 2.0, FireWire, ATA/IDE, RS-232, RS-422,RS-485, 802.11 a/b/g/n, Wi-Fi, WiFi-Direct, Li-Fi, BlueTooth, Ethernet,IrDA, FDDI (fiber distributed data interface), token-ring networks, ormultiplexing techniques based on frequency, time, or code division. Someimplementations may optionally incorporate features such as errorchecking and correction (ECC) for data integrity, or security measures,such as encryption (e.g., WEP) and password protection.

In various embodiments, a computer system may include non-transitorymemory. The memory may be connected to the one or more processors may beconfigured for encoding data and computer readable instructions,including processor executable program instructions. The data andcomputer readable instructions may be accessible to the one or moreprocessors. The processor executable program instructions, when executedby the one or more processors, may cause the one or more processors toperform various operations.

In various embodiments, the computer system may include Internet ofThings (IoT) devices. IoT devices may include objects embedded withelectronics, software, sensors, actuators, and network connectivitywhich enable these objects to collect and exchange data. IoT devices maybe in-use with wired or wireless devices by sending data through aninterface to another device. IoT devices may collect useful data andthen autonomously flow the data between other devices.

A number of implementations have been described. Nevertheless, it willbe understood that various modification may be made. For example,advantageous results may be achieved if the steps of the disclosedtechniques were performed in a different sequence, or if components ofthe disclosed systems were combined in a different manner, or if thecomponents were supplemented with other components. Accordingly, otherimplementations are contemplated within the scope of the followingclaims.

What is claimed is:
 1. A vibration sensing system comprising: avibration sensor configured to sense vibrations of a mechanical unit;and, a data processor operably coupled to receive vibration data fromthe vibration sensor, the data processor configured to performoperations comprising: determining the on-off operational state of themechanical unit based on the received vibration data from the vibrationsensor, gating data processing of the vibration data based on thedetermined on-off operational state of the mechanical unit, in a learnmode, generating at least one threshold as a function of a first set ofcollected vibration data, in a run mode: comparing a second set ofcollected vibration data to the generated at least one threshold, and,outputting at least one indication signal when the second set ofcollected vibration data exceeds the generated at least one threshold.2. The vibration sensing system of claim 1, wherein determining theon-off operational state of the mechanical unit comprises: computing atleast one parameter based on the received vibration data, comparing theat least one parameter to at least one parameter threshold; determiningwhether the at least one parameter exceeds the at least one parameterthreshold; if the at least one parameter exceeds the at least oneparameter threshold, then determining that the mechanical unit is in theon state, if the at least one parameter does not exceed the at least oneparameter threshold, then determining that the mechanical unit is in theoff state.
 3. The vibration sensing system of claim 2, wherein the atleast one parameter comprises at least one root-mean-square (RMS)parameter of the received vibration data and the at least one parameterthreshold comprises at least one RMS threshold.
 4. The vibration sensingsystem of claim 2, wherein the at least one parameter comprises at leastone peak parameter of the received vibration data and the at least oneparameter threshold comprises at least one peak threshold.
 5. Thevibration sensing system of claim 2, wherein the at least one parametercomprises an acceleration parameter and the at least one parameterthreshold comprises an acceleration threshold.
 6. The vibration sensingsystem of claim 2, wherein the at least one parameter comprises avelocity parameter and the at least one parameter threshold comprises avelocity threshold.
 7. The vibration sensing system of claim 2, whereingating data processing of the vibration data based on the determinedon-off operational state of the mechanical unit comprises: enabling dataprocessing of the vibration data when the mechanical unit is determinedto be in an on state, and, disabling data processing of the vibrationdata when the mechanical unit is determined to be in an off state. 8.The vibration sensing system of claim 2, wherein gating data processingof the vibration data based on the determined on-off operational stateof the mechanical unit comprises: enabling data collection of thevibration data when the mechanical unit is determined to be in an onstate, and, disabling data collection of the vibration data when themechanical unit is determined to be in an off state.
 9. The vibrationsensing system of claim 1, wherein the data processor comprises at leastone vibration data filter configured to filter the received vibrationdata from the vibration sensor to generate at least one overall trendingparameter at an output of the at least one vibration data filter, suchthat the at least one overall trending parameter is a filtered functionof the received vibration data from the vibration sensor.
 10. Thevibration sensing system of claim 9, wherein the at least one vibrationdata filter comprises an acceleration data filter.
 11. The vibrationsensing system of claim 10, wherein the acceleration data filtercomprises a high-pass acceleration filter.
 12. The vibration sensingsystem of claim 10, wherein the acceleration data filter comprises ahigh-pass RMS acceleration filter.
 13. The vibration sensing system ofclaim 11, wherein the acceleration data filter comprises a high-passpeak acceleration filter.
 14. The vibration sensing system of claim 9,wherein the at least one vibration data filter comprises a velocity datafilter.
 15. The vibration sensing system of claim 14, wherein thevelocity data filter comprises an RMS velocity filter.
 16. The vibrationsensing system of claim 14, wherein the velocity data filter comprises apeak velocity filter.
 17. The vibration sensing system of claim 9,wherein the data processor further comprises: a first thresholdcomparator configured to compare the at least one overall trendingparameter to the at least one threshold; and, a first filter operablycoupled to an output of the first threshold comparator and configured tooutput the at least one indication signal.
 18. The vibration sensingsystem of claim 17, wherein the first filter outputs the at least oneindication signal when N input events occur within a period M.
 19. Thevibration sensing system of claim 17, wherein the data processor furthercomprises a selector configured to perform time-division multiplexing ofthe at least one overall trending parameter onto the input of the firstthreshold comparator.
 20. The vibration sensing system of claim 9,wherein the data processor further comprises: a first filter operablycoupled to receive the at least one overall trending parameter; and, afirst threshold comparator configured to compare an output of the firstfilter to the at least one threshold and output the at least oneindication signal.
 21. The vibration sensing system of claim 20, whereinthe first filter comprises a N-sample moving average filter configuredto output a moving average of the at least one overall trendingparameter over N samples at the output of the first filter.
 22. Thevibration sensing system of claim 20, wherein the data processor furthercomprises a selector configured to perform time-division multiplexing ofthe at least one overall trending parameter onto the input of the firstfilter.
 23. The vibration sensing system of claim 1, wherein: the atleast one threshold comprises at least one acute alarm threshold and atleast one chronic alarm threshold, the at least one indication signalcomprises at least one acute alarm signal and at least one chronic alarmsignal, and, the data processor is further configured to performoperations comprising: in a learn mode: generating the at least oneacute alarm threshold, and, generating the at least one chronic alarmthreshold, and, in a run mode: comparing the second set of collectedvibration data to the generated at least one acute alarm threshold,outputting the at least one acute alarm signal when the second set ofcollected vibration data exceeds the generated at least one acute alarmthreshold, comparing the second set of collected vibration data to thegenerated at least one chronic alarm threshold, and, outputting the atleast one chronic alarm signal when the second set of collectedvibration data exceeds the generated at least one chronic alarmthreshold.
 24. The vibration sensing system of claim 2, wherein the dataprocessor is further configured to implement disturbance rejection ofother mechanical units that are cross-coupled with the mechanical unitbased on the at least one parameter.
 25. The vibration sensing system ofclaim 24, wherein the at least one parameter threshold is apredetermined function of a level of cross-coupling between othermechanical units cross-coupled with the mechanical unit.
 26. Thevibration sensing system of claim 24, wherein the at least one parameterthreshold comprises: a velocity threshold of at least 0.05 in/sec; and,an acceleration threshold of at least 20 mG.
 27. The vibration sensingsystem of claim 1, further comprising a wireless transmitter node thatoperably couples the vibration sensor with the data processor.
 28. Thevibration sensing system of claim 1, wherein the data processor isconfigured to collect the first set of collected vibration data at adifferent time than the second set of collected vibration data.